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NASA NTRS2019

Influence of Source Propagation Direction and Shear Flow Profile in Impedance Eduction of Acoustic Liners

The acoustic impedance of liners is a key parameter for their design, and depends on the flow conditions, i.e., the sound pressure level and the presence of a grazing flow. The surface impedance of a locally reacting liner is defined as a local intrinsic property relating the acoustic pressure to the normal acoustic particle velocity at the liner surface. Impedance eduction techniques are now widely used to retrieve the impedance of liners in aeroacoustic facilities in the presence of a shear grazing flow. While surface impedance is intrinsic by definition, the educed impedance has recently been shown to depend on the direction of the incident waves relative to the mean flow. Different studies have investigated this issue by considering different acoustic propagation models used in the education process in the hope of matching the educed values. The purpose of the present work is to continue the previous investigations by evaluating the influence of the shear flow profile on the educed impedance, while considering a Bayesian inference process in order to evaluate the uncertainty on the educed values. The identified uncertainties were not able to totally account for the observed discrepancies between educed impedances.

Roncen, R.↗

NASA NTRS2021

Single-Turnover Variable Chlorophyll Fluorescence as a Tool for Assessing Phytoplankton Photosynthesis and Primary Productivity: Opportunities, Caveats and Recommendations

Phytoplankton photosynthetic physiology can be investigated through single-turnover variable chlorophyll fluorescence (ST-ChlF) approaches, which carry unique potential to autonomously collect data at high spatial and temporal resolution. Over the past decades, significant progress has been made in the development and application of ST-ChlF methods in aquatic ecosystems, and in the interpretation of the resulting observations. At the same time, however, an increasing number of sensor types, sampling protocols, and data processing algorithms have created confusion and uncertainty among potential users, with a growing divergence of practice among different research groups. In this review, we assist the existing and upcoming user community by providing an overview of current approaches and consensus recommendations for the use of ST-ChlF measurements to examine in-situ phytoplankton productivity and photo-physiology. We argue that a consistency of practice and adherence to basic operational and quality control standards is critical to ensuring data inter-comparability. Large datasets of inter-comparable and globally coherent ST-ChlF observations hold the potential to reveal large-scale patterns and trends in phytoplankton photo-physiology, photosynthetic rates and bottom-up controls on primary productivity. As such, they hold great potential to provide invaluable physiological observations on the scales relevant for the development and validation of ecosystem models and remote sensing algorithms.

variable chlorophyll fluorescence↗

NASA NTRSDate not supplied

Estimation of Permanent Magnet Synchronous Motor Parameters using a Linear Unknown Input Interval Observer

This paper develops and evaluate a technique to estimate the speed and unknown load torque of a permanent magnet synchronous motor (PMSM). An interval unknown input observer (UIO) for linear time-invariant (LTI) systems is designed and utilized to estimate the PMSM parameters. Interval UIOs are advantageous as they allow for the estimation of both states and unknown inputs, while accounting for varying uncertainty sources, such as model and measurement uncertainties inherent in dynamic systems and processes. First, the nonlinear PMSM model is linearized to transform it to a suitable form for application of the linear interval UIO. Then, the interval UIO is applied to jointly estimate the motor speed and the unknown load torque disturbance. Assuming that the measurement noise and disturbances are bounded, lower and upper bounds are first computed for the unmeasured state (motor speed) and then for the unknown input (load torque). The proposed approach and its limitations are demonstrated for the nonlinear PMSM model derived from its equivalent electrical circuit.

Elinirina I Robinson↗

NASA NTRSDate not supplied

A LaGrangian Perspective of the Surface Energy Budget Contributions to Sea Ice Evolution in the ‘New Arctic’

Recent Arctic warming outpaces global warming and is characterized by dramatic changes in sea ice (record sea ice loss, thinner and more seasonal ice cover), warming SSTs, and a lengthening melt season. These changes impact the surface energy budget and the sensitivity of sea ice to radiative forcing, namely an increased sensitivity of thinner ice to local radiative forcings and a weaker buffering of surface-atmosphere exchanges of heat and moisture. However, these processes are a large source of uncertainty in global climate models with prior studies finding that poor representations of downwelling longwave radiation and sea ice albedo lead to model spread in sea ice conditions. A limitation of prior work is examining sea ice-atmosphere interactions through an Eulerian framework. This study utilizes a sea ice parcel dataset in a LaGrangian framework, allowing for tracking of surface energy budget-sea ice interactions over the lifespan of individual sea ice parcels; this framework also allows for the quantification of the sea ice response to episodic weather events along with seasonal drivers, including the drivers of rapid growth and melt events. First, lag-lead relationships between relevant surface energy budget terms and sea ice thickness changes are explored to understand the atmospheric conditions preceding rapid growth and melt. The impact of episodic events- moisture intrusion, warm air outbreaks, cyclones- on sea ice growth are also investigated by stratifying first-year parcels by sea ice thickness at the end of the growth season and analyzing the effect of atmospheric variability (number and intensity of episodic events) on parcel thickness change. Lastly, parcel growth and melt and the associated radiative anomalies are compared to snowfall events/snow thickness to determine how snowfall modifies the interaction between the local surface energy budget and sea ice mass balance.

Patrick Taylor↗

NASA NTRS2023

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

algal blooms↗

NASA NTRSDate not supplied

Transitioning TEMPO Data for Air Quality Management Applications at the NASA SPoRT Center

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. The NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on stakeholder from air agencies. One component of our work is focused on evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations to characterize the uncertainties and develop best practices for processing and analyzing TEMPO data. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. A SPoRT viewer for TEMPO has been launched for providing visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air agency stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data at our stakeholder agencies. One major goal of our TEMPO initiatives at SPoRT is to better enable the inclusion of TEMPO data in air quality management applications such as exceptional event demonstrations through our close engagement with stakeholders. This talk will provide an update on our SPoRT activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

Air Quality↗

NASA NTRS2025

On the Stability Analysis of Astrophysical Cooling Functions

To model the temperature evolution of optically thin astrophysical environments at MHD scales, radiative and collisional cooling rates are typically either pretabulated or fit into a functional form and then input into MHD codes as a radiative loss function. Thermal balance requires estimates of the analogous heating rates, which are harder to calculate, and due to uncertainties in the underlying dissipative heating processes these rates are often simply parameterized. The resulting net cooling function defines an equilibrium curve that varies with density and temperature. Such cooling functions can make the gas prone to thermal instability (TI), which will cause departures from equilibrium. There has been no systematic study of thermally unstable parameter space for nonequilibrium states. Motivated by our recent finding that there is a related linear instability, catastrophic cooling instability, that can dominate over TI, here we carry out such a study. We show that Balbus instability criteria for TI can be used to define a critical cooling rate, Λc, that permits a nonequilibrium analysis of cooling functions through the mapping of TI zones. We furthermore illustrate how thermal conduction modifies the shape of TI zones. Upon applying a Λc-based stability analysis to coronal loop simulations, we find that loops undergoing periodic episodes of coronal rain formation are linearly unstable to catastrophic cooling instability, while TI is stabilized by thermal conduction.

Amanda Stricklan↗

DOE OSTI2013

Computational Tools to Accelerate Commercial Development

The goals of the work reported are: to develop new computational tools and models to enable industry to more rapidly develop and deploy new advanced energy technologies; to demonstrate the capabilities of the CCSI Toolset on non-proprietary case studies; and to deploy the CCSI Toolset to industry. Challenges of simulating carbon capture (and other) processes include: dealing with multiple scales (particle, device, and whole process scales); integration across scales; verification, validation, and uncertainty; and decision support. The tools cover: risk analysis and decision making; validated, high-fidelity CFD; high-resolution filtered sub-models; process design and optimization tools; advanced process control and dynamics; process models; basic data sub-models; and cross-cutting integration tools.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Computational Tools for Accelerating Carbon Capture Process Development

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

NJOY2016

NJOY2016 is used to convert evaluations in the Evaluated Nuclear Data Files (ENDF) format into forms useful for practical applications such as fission and fusion reactor analysis, criticality safety, radiation shielding, nuclear waste management, nuclear medicine procedures, and more. It begins with the generation of pointwise libraries, including reaction and resonance reconstruction, Doppler broadening, radiation heating and damage, thermal scattering data, unresolved resonance data, and gas production. It reviews the production of libraries for the continuous-energy Monte Carlo code MCNP, multigroup neutron, photon, and particle cross sections and matrices, and photon interaction data. Uncertainty information for ENDF data is processed, including the capability for calculating covariances of resonance data, angular distributions, energy distributions, and radioactive nuclide production. NJOY can prepare and process thermal scattering data evaluations into application libraries. It can also plot cross sections, angular distributions and secondary emission spectra.

Kahler III, A. C. (Skip)↗

DOE OSTI2018

Evaluating the Methodologies of Assessing Long-Term Variability of Wind Speed

Wind resources vary from year to year, and hence the long-term variability of wind speed is a key component of the overall uncertainty in the wind resource assessment process. Because wind-turbine power generation is highly sensitive to wind speed, the variability of wind resources has important implications for renewable power production. As many approaches of calculating the spread of distribution exist, we aim to identify the methods most appropriate for all types of wind-speed distributions. We review and compare various methods, such as the Coefficient of Variation (CoV) and the Robust Coefficient of Variation (RCoV). Because the length of data available also affects the resultant spread, we determine the minimum length of wind-speed records to quantify variability accurately. We use the net monthly energy production of 607 wind farms in the United States from the Energy Information Administration and the wind speeds of the MERRA-2 reanalysis dataset from NASA. After filtering the energy data via linear regression, we assess the accuracy of the variability calculations by comparing the correlations between the wind-speed variability estimates and the variabilities of wind-farm power productions. Results from statistically robust and resistant methods yield higher correlations between wind-speed and energy-production variabilities, as robust methods do not assume Gaussian wind-speed distributions and resistant methods are insensitive to wind speed extremes. We recommend RCoV to systematically estimate long-term variability, with 10 years of monthly mean data to achieve 90% statistical confidence. We will also provide generalized guidance on how to estimate long-term variability including case studies where we identify regions with high wind speeds and low wind-speed RCoVs, such as the Great Plains and the Upper Midwest in central United States.

17 WIND ENERGY↗

DOE OSTI2019

ENDF/B-VIII.0 Augmented Covariance Data: The first iteration

Nuclear data are necessary for reliable modeling and simulation of the next generation of nuclear reactors. However, the variation in the ratio of the computed to experimental values (C/E) for certain types of nuclear systems is much less than predicted by evaluated nuclear data file (ENDF)/B covariances. Figure 1 provides an example of a set of metal-plutonium-fueled, fast-spectrum (PU-MET-FAST) integral experiments from the International Criticality Safety Benchmark Evaluation Project (ICSBEP) in the Oak Ridge National Laboratory (ORNL) VALID database. The variation in the C/E values, shown with one standard deviation error bars, is 100% covered by both the SCALE and ENDB/VIII.0 covariance data. This is due to the comparisons to integral data that are essential during the evaluation process. However, the ENDF evaluations represent uncertainties and correlations in differential data only; they do not reflect the impact of the comparison to integral data in the covariance evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2013

Physics-based dimension reduction in uncertainty quantification for radiative transfer

We present a physics-based methodology for quantifying the uncertainty in a given quantity of interest (QOI) that is contributed by uncertainties in opacities in radiation transport problems. Typically, opacities are tabulated as a function of density, temperature, and photon energy group. The size of this table makes a study of uncertainties at this level challenging because of the well-known 'curse of dimensionality.' We address this by studying uncertain parameters in the underlying physical model that generates the opacity tables. At this level, there are fewer uncertain parameters but still too many to analyze directly through computationally expensive radiation transport simulations. In order to explore this large uncertain parameter space, we develop two simplified radiation transport problems that are much less computationally demanding than the target problem of interest. An emulator is created for each QOI for each simplified problem using Bayesian Multivariate Adaptive Regression Splines (BMARS). This emulator is used to create a functional relationship between the QOIs and the uncertain parameters. Sensitivity analysis is performed using the emulator to determine which parameters contribute significantly to the uncertainty. This physics-based screening process reduces the dimension of the parameter space that is then studied via the computationally expensive radiation transport calculation to generate distributions of quantities of interest. Results of this research demonstrate that the QOIs for the target problem agree for varying screening criteria determined by the sensitivity analysis, and the QOIs agree well for varying Latin Hypercube Design (LHD) sample sizes for the uncertain space. (authors)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

In Situ Spectroscopic Monitoring of MSR Molten Salt and Off-Gas Systems

A significant challenge that must be overcome before commercialization of molten salt reactors is tracking special nuclear material (SNM) through the reactor and subsystems such as fuel chemical processing and off-gas systems. In recent years, several approaches have been explored to track SNM, fission products, and corrosion products in these different subsystems. One such approach is to employ laser and optical spectroscopy to measure the atomic and molecular signals of MSR salts. The advantages of these approaches are that they typically are non-intrusive and offer the capability of process monitoring while still providing low uncertainties. At the Idaho National Laboratory, spectroscopy approaches such as laser-induced breakdown spectroscopy (LIBS) and ultraviolet-visible (UV-Vis) spectroscopy have been utilized to investigate molten salts in an aerosol (LiCl, KCl, RbCl2, NdCl3, and PrCl3) and vapor phases (MgCl5, NdCl5, and ZrCl4) with success. Testing to date has yielded qualitative information on all the above species and quantitative data and analytical figures of merit for species in the vapor phases. These findings, including results from ongoing testing, as well as the experimental conditions and potential applications will be presented.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗

DOE OSTI2023

Estimating building occupancy: a machine learning system for day, night, and episodic events

Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

LOFTID Aeroshell Thermal Response Uncertainty Analysis Utilizing the End-to-End Monte Carlo Approach

A probabilistic thermal margin process has been performed to design the flexible thermal protection system (FTPS) and select entry trajectory constraints given an acceptable risk level for the Low-Earth Orbit Flight Test of and Inflatable Decelerator (LOFTID) project. Uncertainties exist in atmospheric entry aeroheating environments and the predicted thermal response of thermal protection system (TPS) material. Entry vehicle TPS is often over-sized to mitigate uncertainty by combining conservative bounding scenarios together. The probabilistic thermal margin process allows engineers to make informed aeroshell design, flight design, and FTPS performance risk trades while preventing excessive FTPS margin from being applied. This paper describes the uncertainty analysis methodology used to carry out a probabilistic thermal margin process used for LOFTID’s aeroshell and explains how the calculated probability of exceeding flight allowable temperatures is used to design the FTPS and establish nominal flight design constraints. This probabilistic thermal margin process had never been applied to entry, decent, and landing design for a flown entry vehicle and it is one of the LOFTID project’s goals to demonstrate its merits.

Steven Andrew Tobin↗

DOE OSTI2013

Safeguards inventory and process monitoring regulatory comparison

Detecting the theft or diversion of the relatively small amount of fissile material needed to make a nuclear weapon given the normal operating capacity of many of today’s running nuclear production facilities is a difficult task. As throughput increases, the ability of the Material Control and Accountability (MC&A) Program to detect the material loss decreases because the statistical measurement uncertainty also increases. The challenge faced is the ability of current accounting, measurement, and material control programs to detect small yet significant losses under some regulatory approaches can decrease to the point where it is extremely low if not practically non-existent at normal operating capacities. Adding concern to this topic is that there are variations among regulatory bodies as far as what is considered a Significant Quantity (SQ). Some research suggests that thresholds should be lower than those found in any current regulation which if adopted would make meeting detection goals even more difficult. This paper reviews and compares the current regulatory requirements for the MA elements related to physical inventory, uncertainty of the Inventory Difference (ID), and Process Monitoring (PM) in the United States Department of Energy (DOE) and Nuclear Regulatory Commission (NRC), Rosatom of the Russian Federation and the Chinese Atomic Energy Agency (CAEA) of China. The comparison looks at how the regulatory requirements for the implementation of various MA elements perform across a range of operating capacities in example facilities.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2017

Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Lastly, our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.

54 ENVIRONMENTAL SCIENCES↗